Research Assistant Professor
Department of Mathematics, Temple University
Wachman Hall, 1805 N. Broad St., Philadelphia, PA 19122
miksis@temple.edu
miksis@temple.edu
I develop fast, accurate computational methods for multiscale problems in neurophysiology — bridging mathematical modeling, machine learning, and clinical application. My work focuses on building software and techniques that are broadly usable across research and clinical domains.
I am a research assistant professor in the Department of Mathematics at Temple University, working under the mentorship of Gillian Queisser. Previously, I was a postdoctoral research associate in Applied and Computational Mathematics and Statistics at the University of Notre Dame, mentored by Jonathan Hauenstein and Walter Scheirer. I completed my PhD in 2022 at Notre Dame, advised by Yong-Tao Zhang.
Areas of Expertise
- Numerical methods for partial differential equations
- Scientific machine learning and physics-informed models
- High-performance and parallel computing
- Mathematical modeling and simulation
- Applications in neurophysiology, computer vision, and fluid dynamics
Recent Activity
- Spring 2026 — Developing and teaching a new graduate course, MATH 5066: Mathematical Methods for High Performance Computing, at Temple University
- Jan 2026 - Served on DOE\ORISE grant review panel
- Jan 2026 — Released beta version of SPINE (Simulator for Plasticity and Integrated Neuronal Events), an integrated voltage-calcium-synaptic simulator for neuronal networks
- Dec 2025 — Invited seminar at the Fields Institute Centre for Mathematical Medicine, Toronto (video)
- June 2025 - Served on DOE\ASCR grant review panel
Education
PhD, University of Notre Dame 2022
Applied and Computational Mathematics and Statistics
MS, University of Notre Dame 2019
Applied and Computational Mathematics and Statistics
MS, Illinois Institute of Technology 2017
Applied Mathematics
BS, University of Illinois Urbana-Champaign 2014
Mathematics